COVID19_curve
Topic: Modelling the exponential growth of the COVID19 cases in Switzerland.
Numerous reports focus on the development of case numbers over time. But hardly anyone talks about the growth and whether the measures we are taking in the fight against COVID19 are having an effect, in other words: are we beating the virus?
In mathematical epidemiology, time has no influence on the development of case numbers. In the SIR model, the three decisive variables are: Susceptible, Infected, Removed.
Therefore, in my presentation of the case numbers I will show new cases ~ Total confirmed cases. This is a better way to graph cases using a logarithmic scale in “phase space” - plotting the growth rate against the cumulative cases, rather than either of these against time. In this way it can be seen whether the growth is slowing down, i.e. whether the case numbers are no longer developing exponentially.
Inspiration for this presentation comes from this video by minutephysics (https://www.youtube.com/watch?v=54XLXg4fYsc) and the underlying dashboard build by Aatish Bhatia (https://github.com/aatishb/covid/blob/master/curvefit.ipynb).
Here’s Ben Spark explaining the SIR model in a wonderfully simple and understandable manner: https://www.youtube.com/watch?v=k6nLfCbAzgo
Variables:- Date: Date cases were reported
- Cases: Number of people tested positive on that date
- Total_Cases: Cumulative count of cases as of that date
- New_Cases: Number of cases compared to the previous date
- Week: Grouping dates to calendar weeks according to ISO8601
Source: Bundesamt für Gesundheit (BAG), Daten des Situationsberichtes, Last retrieved: 31.03.2020
Loading libraries
library(readxl) # Version ‘1.3.0’
library(dplyr) # Version ‘0.8.4’
library(ggplot2) # Version ‘3.1.0’
library(statR) # Version ‘0.0.0.9000’
library(plotly)Data import
Comment: COVID-19 case numbers since the introduction of mandatory reporting in Switzerland
## # A tibble: 6 x 2
## Date Cases
## <dttm> <dbl>
## 1 2020-02-24 00:00:00 3
## 2 2020-02-25 00:00:00 1
## 3 2020-02-26 00:00:00 14
## 4 2020-02-27 00:00:00 11
## 5 2020-02-28 00:00:00 10
## 6 2020-02-29 00:00:00 17Calculating
Calculating change per day
## # A tibble: 6 x 5
## Date Cases Week Daily_Total Daily_New
## <dttm> <dbl> <chr> <dbl> <dbl>
## 1 2020-02-24 00:00:00 3 2020-09 3 0
## 2 2020-02-25 00:00:00 1 2020-09 4 1
## 3 2020-02-26 00:00:00 14 2020-09 18 14
## 4 2020-02-27 00:00:00 11 2020-09 29 11
## 5 2020-02-28 00:00:00 10 2020-09 39 10
## 6 2020-02-29 00:00:00 17 2020-09 56 17Calculating change per week
## # A tibble: 6 x 3
## # Groups: Week [6]
## Week Weekly_Total Weekly_New
## <chr> <dbl> <dbl>
## 1 2020-09 3 67
## 2 2020-10 102 368
## 3 2020-11 623 2173
## 4 2020-12 3612 6339
## 5 2020-13 10419 7190
## 6 2020-14 16176 36Plotting the curve
Plotting Total Cases vs. New Cases for each day
Plotting Total Cases vs. New Cases for each week
Time is implied. Each point represents the calendar week starting with week 9 on the left and moving to week 14 on the right.
Week 14 currently ongoing. Weekly data not fully available yet.
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